METHODS AND SYSTEMS FOR PREDICTING A HEALTH CONDITION OF A HUMAN SUBJECT

摘要

Disclosed are embodiments of methods and systems for predicting a health condition of a first human subject. The method comprises extracting a historical data including physiological parameters of second human subjects. Thereafter, a first distribution of a first physiological parameter is determined based on a marginal cumulative distribution of a rank transformed historical data. Further, a second distribution of a second physiological parameter is determined based on the first distribution and a first conditional cumulative distribution of the rank transformed historical data. Further, a latent variable is determined based on the first and the second distributions. Thereafter, one or more parameters of at least one bivariate distribution, corresponding to a D-vine copula, are estimated based on the latent variable. Further, a classifier is trained based on the D-vine copula. The classifier is utilizable to predict the health condition of the first human subject based on his/her physiological parameters.

主权项

1. A method for predicting a health condition of a first human subject, the method comprising:
receiving, by one or more processors, a measure of one or more physiological parameters associated with said first human subject, wherein said one or more physiological parameters comprise at least one of an age, a cholesterol level, a heart rate, a blood pressure, a breath carbon-dioxide concentration, a breath oxygen concentration, a stroke score, a blood creatinine level, a blood albumin level, a blood sodium level, a total blood count, a blood glucose/sugar level, a blood haemoglobin level, and a blood platelet count; extracting, by said one or more processors, a historical data comprising a measure of said one or more physiological parameters associated with each of one or more second human subjects; determining, by said one or more processors, a first distribution associated with a first physiological parameter, from said one or more physiological parameters, based on a marginal cumulative distribution of a transformed historical data, wherein said transformed historical data is determined by ranking of said historical data; determining, by said one or more processors, a second distribution associated with a second physiological parameter, from said one or more physiological parameters, based on said first distribution and a first conditional cumulative distribution of said transformed historical data, wherein said first conditional cumulative distribution is deterministic of at least an association between said first physiological parameter and said second physiological parameter; determining, by said one or more processors, a latent variable based at least on said first distribution and said second distribution; estimating, by said one or more processors, one or more parameters of at least one bivariate distribution based on said latent variable, wherein said at least one bivariate distribution corresponds to a D-vine copula, wherein said D-vine copula is deterministic of one or more health conditions associated with each of said one or more second human subjects in said historical data; training, by said one or more processors, a classifier based on said D-vine copula; and predicting, by said one or more processors, said health condition of said first human subject by utilizing said classifier based on said received measure of said one or more physiological parameters associated with said first human subject.